{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de53995b-32ed-4722-8cac-ba104c8efacb",
   "metadata": {},
   "source": [
    "# 导入环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52fac949-4150-4091-b0c3-2968ab5e385c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datasets import Dataset\n",
    "import pandas as pd\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e098d9eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 将JSON文件转换为CSV文件\n",
    "df = pd.read_json('../dataset/huanhuan.json')\n",
    "ds = Dataset.from_pandas(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ac92d42-efae-49b1-a00e-ccaa75b98938",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instruction': ['小姐，别的秀女都在求中选，唯有咱们小姐想被撂牌子，菩萨一定记得真真儿的——',\n",
       "  '这个温太医啊，也是古怪，谁不知太医不得皇命不能为皇族以外的人请脉诊病，他倒好，十天半月便往咱们府里跑。',\n",
       "  '嬛妹妹，刚刚我去府上请脉，听甄伯母说你来这里进香了。'],\n",
       " 'input': ['', '', ''],\n",
       " 'output': ['嘘——都说许愿说破是不灵的。', '你们俩话太多了，我该和温太医要一剂药，好好治治你们。', '出来走走，也是散心。']}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d05e5d-d14e-4f03-92be-9a9677d41918",
   "metadata": {},
   "source": [
    "# 处理数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74ee5a67-2e55-4974-b90e-cbf492de500a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Qwen2Tokenizer(name_or_path='/root/autodl-tmp/qwen/Qwen2-7B-Instruct', vocab_size=151643, model_max_length=131072, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>']}, clean_up_tokenization_spaces=False),  added_tokens_decoder={\n",
       "\t151643: AddedToken(\"<|endoftext|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t151644: AddedToken(\"<|im_start|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "\t151645: AddedToken(\"<|im_end|>\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
       "}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained('/root/autodl-tmp/qwen/Qwen2-7B-Instruct', use_fast=False, trust_remote_code=True)\n",
    "tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2503a5fa-9621-4495-9035-8e7ef6525691",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def process_func(example):\n",
    "    MAX_LENGTH = 384    # Llama分词器会将一个中文字切分为多个token，因此需要放开一些最大长度，保证数据的完整性\n",
    "    input_ids, attention_mask, labels = [], [], []\n",
    "    instruction = tokenizer(f\"<|im_start|>system\\n现在你要扮演皇帝身边的女人--甄嬛<|im_end|>\\n<|im_start|>user\\n{example['instruction'] + example['input']}<|im_end|>\\n<|im_start|>assistant\\n\", add_special_tokens=False)  # add_special_tokens 不在开头加 special_tokens\n",
    "    response = tokenizer(f\"{example['output']}\", add_special_tokens=False)\n",
    "    input_ids = instruction[\"input_ids\"] + response[\"input_ids\"] + [tokenizer.pad_token_id]\n",
    "    attention_mask = instruction[\"attention_mask\"] + response[\"attention_mask\"] + [1]  # 因为eos token咱们也是要关注的所以 补充为1\n",
    "    labels = [-100] * len(instruction[\"input_ids\"]) + response[\"input_ids\"] + [tokenizer.pad_token_id]  \n",
    "    if len(input_ids) > MAX_LENGTH:  # 做一个截断\n",
    "        input_ids = input_ids[:MAX_LENGTH]\n",
    "        attention_mask = attention_mask[:MAX_LENGTH]\n",
    "        labels = labels[:MAX_LENGTH]\n",
    "    return {\n",
    "        \"input_ids\": input_ids,\n",
    "        \"attention_mask\": attention_mask,\n",
    "        \"labels\": labels\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84f870d6-73a9-4b0f-8abf-687b32224ad8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e01fefb791f4a7498f788576b66c2a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/3729 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['input_ids', 'attention_mask', 'labels'],\n",
       "    num_rows: 3729\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_id = ds.map(process_func, remove_columns=ds.column_names)\n",
    "tokenized_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f7e15a0-4d9a-4935-9861-00cc472654b1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<|im_start|>system\\n现在你要扮演皇帝身边的女人--甄嬛<|im_end|>\\n<|im_start|>user\\n小姐，别的秀女都在求中选，唯有咱们小姐想被撂牌子，菩萨一定记得真真儿的——<|im_end|>\\n<|im_start|>assistant\\n嘘——都说许愿说破是不灵的。<|endoftext|>'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(tokenized_id[0]['input_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97f16f66-324a-454f-8cc3-ef23b100ecff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'你们俩话太多了，我该和温太医要一剂药，好好治治你们。<|endoftext|>'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(list(filter(lambda x: x != -100, tokenized_id[1][\"labels\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424823a8-ed0d-4309-83c8-3f6b1cdf274c",
   "metadata": {},
   "source": [
    "# 创建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "170764e5-d899-4ef4-8c53-36f6dec0d198",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11d2f4398fe648e48b60b337674cb49d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Qwen2ForCausalLM(\n",
       "  (model): Qwen2Model(\n",
       "    (embed_tokens): Embedding(152064, 3584)\n",
       "    (layers): ModuleList(\n",
       "      (0-27): 28 x Qwen2DecoderLayer(\n",
       "        (self_attn): Qwen2SdpaAttention(\n",
       "          (q_proj): Linear(in_features=3584, out_features=3584, bias=True)\n",
       "          (k_proj): Linear(in_features=3584, out_features=512, bias=True)\n",
       "          (v_proj): Linear(in_features=3584, out_features=512, bias=True)\n",
       "          (o_proj): Linear(in_features=3584, out_features=3584, bias=False)\n",
       "          (rotary_emb): Qwen2RotaryEmbedding()\n",
       "        )\n",
       "        (mlp): Qwen2MLP(\n",
       "          (gate_proj): Linear(in_features=3584, out_features=18944, bias=False)\n",
       "          (up_proj): Linear(in_features=3584, out_features=18944, bias=False)\n",
       "          (down_proj): Linear(in_features=18944, out_features=3584, bias=False)\n",
       "          (act_fn): SiLU()\n",
       "        )\n",
       "        (input_layernorm): Qwen2RMSNorm()\n",
       "        (post_attention_layernorm): Qwen2RMSNorm()\n",
       "      )\n",
       "    )\n",
       "    (norm): Qwen2RMSNorm()\n",
       "  )\n",
       "  (lm_head): Linear(in_features=3584, out_features=152064, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained('/root/autodl-tmp/qwen/Qwen2-7B-Instruct', device_map=\"auto\",torch_dtype=torch.bfloat16)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2323eac7-37d5-4288-8bc5-79fac7113402",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f808b05c-f2cb-48cf-a80d-0c42be6051c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.bfloat16"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d71257-3c1c-4303-8ff8-af161ebc2cf1",
   "metadata": {},
   "source": [
    "# lora "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d304ae2-ab60-4080-a80d-19cac2e3ade3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type=<TaskType.CAUSAL_LM: 'CAUSAL_LM'>, inference_mode=False, r=8, target_modules={'o_proj', 'down_proj', 'q_proj', 'gate_proj', 'up_proj', 'k_proj', 'v_proj'}, lora_alpha=32, lora_dropout=0.1, fan_in_fan_out=False, bias='none', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}, use_dora=False, layer_replication=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from peft import LoraConfig, TaskType, get_peft_model\n",
    "\n",
    "config = LoraConfig(\n",
    "    task_type=TaskType.CAUSAL_LM, \n",
    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
    "    inference_mode=False, # 训练模式\n",
    "    r=8, # Lora 秩\n",
    "    lora_alpha=32, # Lora alaph，具体作用参见 Lora 原理\n",
    "    lora_dropout=0.1# Dropout 比例\n",
    ")\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c2489c5-eaab-4e1f-b06a-c3f914b4bf8e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LoraConfig(peft_type=<PeftType.LORA: 'LORA'>, auto_mapping=None, base_model_name_or_path='/root/autodl-tmp/qwen/Qwen2-7B-Instruct', revision=None, task_type=<TaskType.CAUSAL_LM: 'CAUSAL_LM'>, inference_mode=False, r=8, target_modules={'o_proj', 'down_proj', 'q_proj', 'gate_proj', 'up_proj', 'k_proj', 'v_proj'}, lora_alpha=32, lora_dropout=0.1, fan_in_fan_out=False, bias='none', use_rslora=False, modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}, use_dora=False, layer_replication=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = get_peft_model(model, config)\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ebf5482b-fab9-4eb3-ad88-c116def4be12",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 20,185,088 || all params: 7,635,801,600 || trainable%: 0.26434798934534914\n"
     ]
    }
   ],
   "source": [
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca055683-837f-4865-9c57-9164ba60c00f",
   "metadata": {},
   "source": [
    "# 配置训练参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e76bbff-15fd-4995-a61d-8364dc5e9ea0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "args = TrainingArguments(\n",
    "    output_dir=\"./output/Qwen2_instruct_lora\",\n",
    "    per_device_train_batch_size=4,\n",
    "    gradient_accumulation_steps=4,\n",
    "    logging_steps=10,\n",
    "    num_train_epochs=3,\n",
    "    save_steps=10, # 为了快速演示，这里设置10，建议你设置成100\n",
    "    learning_rate=1e-4,\n",
    "    save_on_each_node=True,\n",
    "    gradient_checkpointing=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f142cb9c-ad99-48e6-ba86-6df198f9ed96",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=args,\n",
    "    train_dataset=tokenized_id,\n",
    "    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec9bc36-b297-45af-99e1-d4c4d82be081",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abb2327-458e-4e96-ac98-2141b5b97c8e",
   "metadata": {},
   "source": [
    "# 合并加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd2a415a-a9ad-49ea-877f-243558a83bfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9433e3ebd2d4e3c804f032223096b53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n",
      "WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "臣妾是大理寺少卿甄远道之女，大理寺少卿甄远道之女，家父现任大理寺少卿，家母是大理寺少卿甄远道之妻。\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "import torch\n",
    "from peft import PeftModel\n",
    "\n",
    "mode_path = '/root/autodl-tmp/qwen/Qwen2-7B-Instruct/'\n",
    "lora_path = './output/Qwen2_instruct_lora/checkpoint-10' # 这里改称你的 lora 输出对应 checkpoint 地址\n",
    "\n",
    "# 加载tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(mode_path, trust_remote_code=True)\n",
    "\n",
    "# 加载模型\n",
    "model = AutoModelForCausalLM.from_pretrained(mode_path, device_map=\"auto\",torch_dtype=torch.bfloat16, trust_remote_code=True).eval()\n",
    "\n",
    "# 加载lora权重\n",
    "model = PeftModel.from_pretrained(model, model_id=lora_path)\n",
    "\n",
    "prompt = \"你是谁？\"\n",
    "inputs = tokenizer.apply_chat_template([{\"role\": \"user\", \"content\": \"假设你是皇帝身边的女人--甄嬛。\"},{\"role\": \"user\", \"content\": prompt}],\n",
    "                                       add_generation_prompt=True,\n",
    "                                       tokenize=True,\n",
    "                                       return_tensors=\"pt\",\n",
    "                                       return_dict=True\n",
    "                                       ).to('cuda')\n",
    "\n",
    "\n",
    "gen_kwargs = {\"max_length\": 2500, \"do_sample\": True, \"top_k\": 1}\n",
    "with torch.no_grad():\n",
    "    outputs = model.generate(**inputs, **gen_kwargs)\n",
    "    outputs = outputs[:, inputs['input_ids'].shape[1]:]\n",
    "    print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f88a87c6-e330-430b-bd24-69f5180034c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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